Hostile intercontinental missiles can be expected to deploy decoy objects to conceal the warhead(s). The effectiveness of hit-to-kill based weapon systems against a target object of interest surrounded by decoy and other objects depends upon discriminating the object of interest from decoys and other objects of a “cloud.” Discriminating this object of interest may involve the use of radar as a radio frequency (RF) sensor and the use of an interceptor missile fitted with a sensor such as an infrared (IR) tracker or sensor. The information from either the RF or the IR sensor is used to discriminate among the objects. Fusing discrimination information from both the RF and IR sensors increases the probability of discriminating and selecting the object of interest and thus improves the likelihood of successful engagement to negate the threat.
The challenges in this approach are to correlate the objects of the cloud as sensed by the separate sensors, and properly account for the resulting probability of correlation in the fused discrimination results. For example, if the probability of correlation is zero (i.e. objects from the RF sensor cannot be to corresponding objects from the IR sensor), the RF discrimination results cannot be trusted and the fused discrimination solution should only depend upon or use the IR discrimination results. Alternately, if the probability of correlation is non-zero, the fused discrimination results should be a blend of both the RF and IR discrimination results.
Many discrimination fusion systems are in use today, but they are primarily heuristic in nature and are based on a set of rules that govern how the discrimination results are fused given the number of objects in common between the sensors and the degree of confidence in the discrimination solutions from each sensor. The current correlation methodology for the Standard Missile-3 is based on such a rule set.
Improved or alternative real-time object selection systems based on multi-sensor discrimination and correlation are desired.